Top Deep Learning Frameworks of 2019 and How Do They Compare

From personalizing customer experience to automating processes, Deep Learning applications are offering smart solutions to businesses across industries, opening up a world of opportunities for them. Deep Learning algorithms use sophisticated structures, such as Convolutional Neural Networks, belief networks, or recurrent neural networks. Effective DL frameworks also help simplify the implementation of large and complex models like Convolutional Neural Networks.

In this post, we present the top Deep Learning frameworks preferred by data scientists and Deep Learning experts across the globe. We have also included the major pros and cons of each framework, enabling you to choose the right one for your upcoming project.

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1. TensorFlow

Google’s TensorFlow is a mature Python-friendly open-source Deep Learn

ing framework with great community support, large repository, and extensive usage. It is primarily used for numerical computation and large-scale Machine Learning.

TensorFlow’s ability to train and run deep neural networks for handwritten digit classification, image recognition, and word embeddings among others has made it a popular framework among Deep Learning professionals.

Pros

• Continued Community Support

TensorFlow is developed and maintained by Google as it powers many features of Google applications, namely recognizing spoken words, translating from one language to the other, and improving search results. Thus, TensorFlow has the advantage of seamless performance, quick updates, and frequent new releases.

• Superior Computational Graph Visualizations

TensorFlow has better computational graph visualizations when compared to Theano and Torch.

• Offers Great Debugging Potential

With TensorFlow, you can easily introduce and retrieve the results of discrete data on to an edge and combine this with TensorBoard to get an easy-to-understand graph visualization. This makes debugging even simpler.

• Easy to Spin Up Sessions

Since TensorFlow can run with multiple GPUs, it is easy to spin up sessions and run the code on different machines without the need to stop or restart the program.

• It’s Written in Python

The nodes and tensors in TensorFlow are all Python objects. All the high-level abstractions and development in TensorFlow are done using Python which is an easy language to read and develop in.

Cons

• TensorFlow Is Not Fully Open Source

Presently, Google has partly open-sourced TensorFlow. Thus, a few algorithms are open-source but the advanced hardware infrastructure isn’t.

• Isn’t Compatible with Windows

TensorFlow doesn’t work on Windows. Hence, it misses out on a wide variety of Windows users. These users need to install TensorFlow using the python package library.

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2. Keras

Keras prides itself for its user-friendliness and easy prototyping. It is a high-level neural network API that allows for intuitive and fast experimentation.

Pros

• Simple to Use

Keras is user-friendly and enforces minimalism, making it easy to write a small neural network in short pieces of code. Anyone who is familiar with Deep Learning can easily work with Keras.

• Large and Active Community

Thanks to its user-friendliness and excellent documentation, Keras enjoys a large and active community. Further, Keras is powered by Python and enjoys TensorFlow support. As a result, the framework is soaring in popularity.

• It’s Easy to Choose the Backend for Keras

Keras sits atop TensorFlow or CNTK, providing an intuitive API inspired by Torch. So, if you want to choose a backend, simply change the field to any of these frameworks.

Cons

• Limited Customization

Though Keras is quite modular, it’s not possible to customize your DL model beyond a point. You need to use Theano or TensorFlow when building something beyond the application level.

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3. PyTorch

Though PyTorch is similar to Tensorflow in many ways, it is considered to be far more researcher-friendly, offering a highly interactive development model. This Deep Learning framework developed by Facebook’s AI research group is generally a better choice for projects that need to be up and running within a short time. However, TensorFlow wins for larger projects with complex workflows.

Pros

• Runs on Python

Anyone with the basic understanding of Python can build their own first Deep Learning models with ease. PyTorch shines for its rapid prototyping with DNNs, auto-differentiation, deep Python integration, and strong support for GPUs.

• Less Training Time

PyTorch offers faster Deep Learning training as compared to other frameworks.

• Fairly Simple and Transparent

PyTorch’s framework has an architectural style, making the Deep Learning model process simple, user-friendly, and transparent compared to other frameworks.

• Easy to Debug

PyTorch allows easy debugging in case you find an issue in the network. The framework uses PyCharm for define-by-run mode during runtime.

Cons

• Limited Resources

Since PyTorch is relatively new in the list of Deep Learning frameworks, it has fewer users at this point. Further, it has limited references and resources outside of the official documentation.

• Lacks Interfaces for Monitoring and Visualization

At present, PyTorch lacks interfaces for monitoring and visualization like TensorBoard. However, it allows users to connect externally to TensorBoard.

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4. CNTK

Computational Network Toolkit by Microsoft Research is an open-source Deep Learning framework that describes neural networks as a series of computational steps through a directed graph. The framework allows Deep Learning professionals to realize and combine popular model types, namely feed-forward DNNs, convolutional nets (CNNs), and recurrent networks.

Pros

• Highly Flexible 

Besides offering a wide variety of built-in computation nodes, CNTK provides a plug-in architecture, allowing you to define your own computation nodes. Thus, if your workload requires special customization, you can use CNTK.

• Allows Distributed Training

Training Deep Learning models can be time-intensive. CNTK ensures enhanced performance for CPUs, single-GPU, multi-GPU, and multi-machine-multi-GPU scenarios. The framework allows highly-scalable parallel training on a large number of GPUs spanning multiple machines.

Cons

• Lacks Visualization

We often want to visualize complex neural networks as it helps us debug performance bottlenecks. However, CNTK lacks visualization and mobile ARM support, making it tough for users to get insights into how the various layers of the network are connected.

5. Apache MXNet

MXNet is quite popular among Java users who prefer writing moderately large code from scratch. Adopted by Amazon Web Services, this Deep Learning framework can scale linearly across multiple GPUs and machines.

Pros

• Supports Many Languages

MXNet supports a plethora of languages, namely including R, C++, Python, Julia, and Scala among others. Thus, it can work with multiple servers at once, making the learning process quicker.

• Fast, Flexible, and Efficient When Running DL Algorithms

MXNet features advanced GPU support and has a high-performance imperative API. The framework is widely popular for its scalability and used by organizations for speech and handwriting recognition, NLP, and forecasting.

Cons

• Small Community 

MXNet is a great framework for big industrial projects but has a smaller community compared to TensorFlow. Since it isn’t popular among the research community you may not receive support when you need it the most.

Summing Up

Frameworks often work as a library, providing the necessary architecture for DL professionals to develop models with ease. Due to the convenience and support they offer, Deep Learning frameworks have become extremely popular among DL and ML professionals.

However, it can be tough to choose the most suitable framework for a project among a sea of choices available today. Therefore, it is necessary to have a sound knowledge of all the available frameworks along with their pros and cons, enabling you to choose the most beneficial Deep Learning framework for your upcoming project.

Use the information shared in this post to find the best framework when building your next Deep Learning model.

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